🤖 AI Summary
Existing Evidential Deep Learning (EDL) relies on the standard Dirichlet distribution to model class-wise uncertainty, but its strong parametric assumptions hinder generalization and robustness under complex, long-tailed, and noisy data conditions. To address this, we propose Flexible Evidential Deep Learning (F-EDL), which replaces the standard Dirichlet with the generalized Dirichlet distribution—thereby relaxing structural constraints and enabling more adaptive, flexible modeling of class probability uncertainty. F-EDL employs a neural network to directly predict the parameters of the generalized Dirichlet, preserving computational efficiency while significantly improving robustness to distributional shifts. Extensive experiments demonstrate that F-EDL achieves state-of-the-art performance in uncertainty estimation across standard, long-tailed, and noisy benchmarks. Notably, it enhances model reliability and trustworthiness in high-stakes applications where calibrated uncertainty is critical.
📝 Abstract
Uncertainty quantification (UQ) is crucial for deploying machine learning models in high-stakes applications, where overconfident predictions can lead to serious consequences. An effective UQ method must balance computational efficiency with the ability to generalize across diverse scenarios. Evidential deep learning (EDL) achieves efficiency by modeling uncertainty through the prediction of a Dirichlet distribution over class probabilities. However, the restrictive assumption of Dirichlet-distributed class probabilities limits EDL's robustness, particularly in complex or unforeseen situations. To address this, we propose extit{flexible evidential deep learning} ($mathcal{F}$-EDL), which extends EDL by predicting a flexible Dirichlet distribution -- a generalization of the Dirichlet distribution -- over class probabilities. This approach provides a more expressive and adaptive representation of uncertainty, significantly enhancing UQ generalization and reliability under challenging scenarios. We theoretically establish several advantages of $mathcal{F}$-EDL and empirically demonstrate its state-of-the-art UQ performance across diverse evaluation settings, including classical, long-tailed, and noisy in-distribution scenarios.